Instructions to use wetsoledrysoul/intermediate_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wetsoledrysoul/intermediate_checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="wetsoledrysoul/intermediate_checkpoints") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("wetsoledrysoul/intermediate_checkpoints") model = AutoModelForCausalLM.from_pretrained("wetsoledrysoul/intermediate_checkpoints", 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 wetsoledrysoul/intermediate_checkpoints with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "wetsoledrysoul/intermediate_checkpoints" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wetsoledrysoul/intermediate_checkpoints", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/wetsoledrysoul/intermediate_checkpoints
- SGLang
How to use wetsoledrysoul/intermediate_checkpoints 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 "wetsoledrysoul/intermediate_checkpoints" \ --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": "wetsoledrysoul/intermediate_checkpoints", "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 "wetsoledrysoul/intermediate_checkpoints" \ --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": "wetsoledrysoul/intermediate_checkpoints", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use wetsoledrysoul/intermediate_checkpoints with Docker Model Runner:
docker model run hf.co/wetsoledrysoul/intermediate_checkpoints
End of training
Browse files- README.md +66 -0
- chat_template.jinja +16 -0
- config.json +69 -0
- generation_config.json +14 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +13 -0
- training_args.bin +3 -0
README.md
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---
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library_name: transformers
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model_name: intermediate_checkpoints
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tags:
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- generated_from_trainer
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- grpo
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- trl
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licence: license
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---
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# Model Card for intermediate_checkpoints
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This model is a fine-tuned version of [None](https://huggingface.co/None).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="wetsoledrysoul/intermediate_checkpoints", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/CodeShield/CerebRM-GRPO-0925/runs/tdy36n7w)
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This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
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### Framework versions
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- TRL: 1.7.1
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- Transformers: 5.12.1
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- Pytorch: 2.11.0+cu128
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- Datasets: 5.0.0
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- Tokenizers: 0.22.2
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## Citations
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Cite GRPO as:
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```bibtex
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@article{shao2024deepseekmath,
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title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
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author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
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year = 2024,
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eprint = {arXiv:2402.03300},
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}
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```
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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chat_template.jinja
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{%- set has_system = messages|selectattr('role', 'equalto', 'system')|list|length > 0 -%}{%- if not has_system -%}{{- '<|im_start|>system
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You are a helpful function-calling AI assistant. ' -}}{%- if tools is none or (tools | length) == 0 -%}{{- 'You do not currently have access to any functions. <functions></functions><|im_end|>
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| 3 |
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' -}}{%- else -%}{{- 'You are provided with function signatures within <functions></functions> XML tags. You may call one or more functions to assist with the user query. Output any function calls within <function_calls></function_calls> XML tags. Do not make assumptions about what values to plug into functions.' -}}{{- '<functions>' -}}{{- tools | tojson -}}{{- '</functions><|im_end|>
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' -}}{%- endif -%}{%- endif -%}{%- for message in messages -%}{%- if message['role'] == 'system' -%}{{- '<|im_start|>system
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' + message['content'] -}}{%- if tools is not none -%}{{- '<functions>' -}}{{- tools | tojson -}}{{- '</functions>' -}}{%- elif message.get('functions', none) is not none -%}{{- ' <functions>' + message['functions'] + '</functions>' -}}{%- endif -%}{{- '<|im_end|>
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' -}}{%- elif message['role'] == 'user' -%}{{- '<|im_start|>user
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| 7 |
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' + message['content'] + '<|im_end|>
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| 8 |
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' -}}{%- elif message['role'] == 'assistant' -%}{{- '<|im_start|>assistant
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| 9 |
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' -}}{%- if message.get('content', none) is not none -%}{{- message['content'] -}}{%- endif -%}{%- if message.get('function_calls', none) is not none -%}{{- '<function_calls>' + message['function_calls'] + '</function_calls>' -}}{% elif message.get('tool_calls', none) is not none %}{{- '<function_calls>' -}}{%- for tool_call in message['tool_calls'] %}{%- if tool_call is mapping and tool_call.get('function', none) is not none %}{%- set args = tool_call['function']['arguments'] -%}{%- set ns = namespace(arguments_list=[]) -%}{%- for key, value in args.items() -%}{%- set ns.arguments_list = ns.arguments_list + [key ~ '=' ~ (value | tojson)] -%}{%- endfor -%}{%- set arguments = ns.arguments_list | join(', ') -%}{{- tool_call['function']['name'] + '(' + arguments + ')' -}}{%- if not loop.last -%}{{ '
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| 10 |
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' }}{%- endif -%}{% else %}{{- tool_call -}}{%- endif %}{%- endfor %}{{- '</function_calls>' -}}{%- endif -%}{%- if not loop.last -%}{{- '<|im_end|>' + '
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' -}}{%- else -%}{{- eos_token -}}{%- endif -%}{%- elif message['role'] == 'environment' -%}{{- '<|im_start|>environment
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' + message['content'] + '<|im_end|>
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' -}}{%- elif message['role'] == 'tool' -%}{{- '<|im_start|>environment
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' + message['content'] + '<|im_end|>
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' -}}{%- endif -%}{%- if loop.last and add_generation_prompt -%}{{- '<|im_start|>assistant
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' -}}{%- endif -%}{%- endfor -%}
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config.json
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{
|
| 2 |
+
"architectures": [
|
| 3 |
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"Olmo3ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 100257,
|
| 8 |
+
"dtype": "bfloat16",
|
| 9 |
+
"eos_token_id": 100257,
|
| 10 |
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"hidden_act": "silu",
|
| 11 |
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"hidden_size": 4096,
|
| 12 |
+
"initializer_range": 0.02,
|
| 13 |
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"intermediate_size": 11008,
|
| 14 |
+
"layer_types": [
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| 15 |
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"sliding_attention",
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| 16 |
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"sliding_attention",
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| 17 |
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"sliding_attention",
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| 18 |
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"full_attention",
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| 19 |
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"sliding_attention",
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| 20 |
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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| 24 |
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"sliding_attention",
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"sliding_attention",
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| 26 |
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"full_attention",
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| 27 |
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"sliding_attention",
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| 28 |
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"sliding_attention",
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| 29 |
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"sliding_attention",
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| 30 |
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"full_attention",
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| 31 |
+
"sliding_attention",
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| 32 |
+
"sliding_attention",
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| 33 |
+
"sliding_attention",
|
| 34 |
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"full_attention",
|
| 35 |
+
"sliding_attention",
|
| 36 |
+
"sliding_attention",
|
| 37 |
+
"sliding_attention",
|
| 38 |
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"full_attention",
|
| 39 |
+
"sliding_attention",
|
| 40 |
+
"sliding_attention",
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| 41 |
+
"sliding_attention",
|
| 42 |
+
"full_attention",
|
| 43 |
+
"sliding_attention",
|
| 44 |
+
"sliding_attention",
|
| 45 |
+
"sliding_attention",
|
| 46 |
+
"full_attention"
|
| 47 |
+
],
|
| 48 |
+
"max_position_embeddings": 65536,
|
| 49 |
+
"model_type": "olmo3",
|
| 50 |
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"num_attention_heads": 32,
|
| 51 |
+
"num_hidden_layers": 32,
|
| 52 |
+
"num_key_value_heads": 32,
|
| 53 |
+
"pad_token_id": 100277,
|
| 54 |
+
"rms_norm_eps": 1e-06,
|
| 55 |
+
"rope_parameters": {
|
| 56 |
+
"attention_factor": 1.2079441541679836,
|
| 57 |
+
"beta_fast": 32,
|
| 58 |
+
"beta_slow": 1,
|
| 59 |
+
"factor": 8.0,
|
| 60 |
+
"original_max_position_embeddings": 8192,
|
| 61 |
+
"rope_theta": 500000,
|
| 62 |
+
"rope_type": "yarn"
|
| 63 |
+
},
|
| 64 |
+
"sliding_window": 4096,
|
| 65 |
+
"tie_word_embeddings": false,
|
| 66 |
+
"transformers_version": "5.12.1",
|
| 67 |
+
"use_cache": false,
|
| 68 |
+
"vocab_size": 100278
|
| 69 |
+
}
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generation_config.json
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{
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| 2 |
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"_from_model_config": true,
|
| 3 |
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"bos_token_id": 100257,
|
| 4 |
+
"do_sample": true,
|
| 5 |
+
"eos_token_id": [
|
| 6 |
+
100265,
|
| 7 |
+
100257
|
| 8 |
+
],
|
| 9 |
+
"max_new_tokens": 32768,
|
| 10 |
+
"pad_token_id": 100277,
|
| 11 |
+
"temperature": 0.6,
|
| 12 |
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"top_p": 0.95,
|
| 13 |
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"transformers_version": "5.12.1"
|
| 14 |
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8fee37e8c1256e30c8fab630b3032cfaaac5b7d964a8208325ca092409489cdb
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| 3 |
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size 14596063960
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tokenizer.json
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tokenizer_config.json
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{
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| 2 |
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"add_prefix_space": false,
|
| 3 |
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"backend": "tokenizers",
|
| 4 |
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"bos_token": "<|endoftext|>",
|
| 5 |
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"clean_up_tokenization_spaces": false,
|
| 6 |
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"eos_token": "<|endoftext|>",
|
| 7 |
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"is_local": true,
|
| 8 |
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"local_files_only": false,
|
| 9 |
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"model_max_length": 65536,
|
| 10 |
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"pad_token": "<|pad|>",
|
| 11 |
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"tokenizer_class": "TokenizersBackend",
|
| 12 |
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"unk_token": "<|endoftext|>"
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| 13 |
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}
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training_args.bin
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:3f305423f5d0a50eec9c5c26e4d5344caae8eef3ebf4286283142f3365ba455e
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| 3 |
+
size 9553
|