Text Generation
Transformers
Safetensors
qwen3
on-policy-distillation
multi-teacher
math
search
tool-use
conversational
text-generation-inference
Instructions to use willamazon1/mopd-sdft-iter200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use willamazon1/mopd-sdft-iter200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="willamazon1/mopd-sdft-iter200") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("willamazon1/mopd-sdft-iter200") model = AutoModelForCausalLM.from_pretrained("willamazon1/mopd-sdft-iter200", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use willamazon1/mopd-sdft-iter200 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "willamazon1/mopd-sdft-iter200" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "willamazon1/mopd-sdft-iter200", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/willamazon1/mopd-sdft-iter200
- SGLang
How to use willamazon1/mopd-sdft-iter200 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "willamazon1/mopd-sdft-iter200" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "willamazon1/mopd-sdft-iter200", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "willamazon1/mopd-sdft-iter200" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "willamazon1/mopd-sdft-iter200", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use willamazon1/mopd-sdft-iter200 with Docker Model Runner:
docker model run hf.co/willamazon1/mopd-sdft-iter200
Add mopd-sdft-iter200 (multi-teacher OPD, light-SFT student init, iter 200)
Browse files- README.md +56 -0
- config.json +30 -0
- generation_config.json +7 -0
- merges.txt +0 -0
- model-00000-of-00004.safetensors +3 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model.safetensors.index.json +406 -0
- tokenizer.json +0 -0
- tokenizer_config.json +239 -0
- vocab.json +0 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: apache-2.0
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base_model:
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- Qwen/Qwen3-8B-Base
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- on-policy-distillation
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- multi-teacher
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| 10 |
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- math
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| 11 |
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- search
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| 12 |
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- tool-use
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| 13 |
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---
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| 14 |
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| 15 |
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# mopd-sdft-iter200
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A Qwen3-8B model trained with **multi-teacher On-Policy Distillation (OPD)** over a mixed
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math + search + tool-use (tau) stream.
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| 19 |
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## Training
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- **Architecture:** Qwen3-8B (36 layers, hidden 4096, FFN 12288, 32 query / 8 KV heads,
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| 23 |
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head_dim 128, vocab 151936, QK-layernorm, untied embeddings, RoPE θ=1e6).
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| 24 |
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- **Student initialization:** a light supervised-finetuned base
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| 25 |
+
(`Qwen3-8B-oracle-mix-SFT`, balanced oracle-mix, iteration 500) derived from
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| 26 |
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`Qwen/Qwen3-8B-Base`. This light-SFT start gives the student the tool-call /
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| 27 |
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instruction-following priors needed to emit trainable agentic trajectories before OPD.
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- **Method:** On-policy distillation. A single student rolls out a *mixed*
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| 29 |
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math + search + tau trajectory stream; a static per-sample domain tag routes each
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| 30 |
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trajectory to a domain-specific teacher (math / search / tau). The only training
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| 31 |
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signal is the per-token reverse-KL from the student to its domain teacher
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| 32 |
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(task reward = 0).
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| 33 |
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- **Checkpoint:** iteration 200.
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+
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| 35 |
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## Usage
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```python
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| 38 |
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from transformers import AutoModelForCausalLM, AutoTokenizer
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| 39 |
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import torch
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| 40 |
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model_id = "willamazon1/mopd-sdft-iter200"
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tok = AutoTokenizer.from_pretrained(model_id)
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| 43 |
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model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
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| 44 |
+
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| 45 |
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msgs = [{"role": "user", "content": "What is 12*8?"}]
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| 46 |
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text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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| 47 |
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ids = tok(text, return_tensors="pt").input_ids.to(model.device)
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| 48 |
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out = model.generate(ids, max_new_tokens=64, do_sample=False)
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| 49 |
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print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
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```
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## Notes
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- Weights are the converted HuggingFace `safetensors` export (bf16, 399 tensors,
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verified free of NaN/Inf) of a Megatron `torch_dist` training checkpoint.
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- Tokenizer and config are inherited from the Qwen3-8B lineage.
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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| 7 |
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"bos_token_id": 151643,
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| 8 |
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"eos_token_id": 151643,
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| 9 |
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"head_dim": 128,
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| 10 |
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"hidden_act": "silu",
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| 11 |
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"hidden_size": 4096,
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| 12 |
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"initializer_range": 0.02,
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| 13 |
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"intermediate_size": 12288,
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| 14 |
+
"max_position_embeddings": 32768,
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| 15 |
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"max_window_layers": 36,
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"model_type": "qwen3",
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| 17 |
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"num_attention_heads": 32,
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| 18 |
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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| 20 |
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"rms_norm_eps": 1e-06,
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| 21 |
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"rope_scaling": null,
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"rope_theta": 1000000,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": false,
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| 4 |
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"eos_token_id": 151643,
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| 5 |
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"max_new_tokens": 2048,
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| 6 |
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"transformers_version": "4.37.0"
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}
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merges.txt
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The diff for this file is too large to render.
See raw diff
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model-00000-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6cc3bb7f701dd91b9a112bd4ab481499ece7cd447cd00cf7a34f54691717e2c9
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size 5271821192
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model-00001-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:cb749d060d0a3b4d812f223909c6a5da767f0b6e6fe1d90cdc5d6f3689c4f13e
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size 5271217784
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model-00002-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:10fc29c5b7c6631c8aa07b5f227f0e5c68f2027f24ddc99b20e8f10f76e9bad2
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size 5335160760
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model-00003-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f9fbedfb8e0cdcfd07e4e983f5c93e39fddd311a7c10284b833b55fd8595982a
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size 503317048
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model.safetensors.index.json
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tokenizer.json
ADDED
|
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|
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|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,239 @@
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"151666": {
|
| 190 |
+
"content": "</tool_response>",
|
| 191 |
+
"lstrip": false,
|
| 192 |
+
"normalized": false,
|
| 193 |
+
"rstrip": false,
|
| 194 |
+
"single_word": false,
|
| 195 |
+
"special": false
|
| 196 |
+
},
|
| 197 |
+
"151667": {
|
| 198 |
+
"content": "<think>",
|
| 199 |
+
"lstrip": false,
|
| 200 |
+
"normalized": false,
|
| 201 |
+
"rstrip": false,
|
| 202 |
+
"single_word": false,
|
| 203 |
+
"special": false
|
| 204 |
+
},
|
| 205 |
+
"151668": {
|
| 206 |
+
"content": "</think>",
|
| 207 |
+
"lstrip": false,
|
| 208 |
+
"normalized": false,
|
| 209 |
+
"rstrip": false,
|
| 210 |
+
"single_word": false,
|
| 211 |
+
"special": false
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"additional_special_tokens": [
|
| 215 |
+
"<|im_start|>",
|
| 216 |
+
"<|im_end|>",
|
| 217 |
+
"<|object_ref_start|>",
|
| 218 |
+
"<|object_ref_end|>",
|
| 219 |
+
"<|box_start|>",
|
| 220 |
+
"<|box_end|>",
|
| 221 |
+
"<|quad_start|>",
|
| 222 |
+
"<|quad_end|>",
|
| 223 |
+
"<|vision_start|>",
|
| 224 |
+
"<|vision_end|>",
|
| 225 |
+
"<|vision_pad|>",
|
| 226 |
+
"<|image_pad|>",
|
| 227 |
+
"<|video_pad|>"
|
| 228 |
+
],
|
| 229 |
+
"bos_token": null,
|
| 230 |
+
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
| 231 |
+
"clean_up_tokenization_spaces": false,
|
| 232 |
+
"eos_token": "<|endoftext|>",
|
| 233 |
+
"errors": "replace",
|
| 234 |
+
"model_max_length": 131072,
|
| 235 |
+
"pad_token": "<|endoftext|>",
|
| 236 |
+
"split_special_tokens": false,
|
| 237 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 238 |
+
"unk_token": null
|
| 239 |
+
}
|
vocab.json
ADDED
|
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|
|
|