Instructions to use CapiJack/UltronTactIQ-Mistral-2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CapiJack/UltronTactIQ-Mistral-2-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CapiJack/UltronTactIQ-Mistral-2-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CapiJack/UltronTactIQ-Mistral-2-7b")
model = AutoModelForCausalLM.from_pretrained("CapiJack/UltronTactIQ-Mistral-2-7b")
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 CapiJack/UltronTactIQ-Mistral-2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CapiJack/UltronTactIQ-Mistral-2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CapiJack/UltronTactIQ-Mistral-2-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CapiJack/UltronTactIQ-Mistral-2-7b

SGLang

How to use CapiJack/UltronTactIQ-Mistral-2-7b 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 "CapiJack/UltronTactIQ-Mistral-2-7b" \
    --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": "CapiJack/UltronTactIQ-Mistral-2-7b",
		"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 "CapiJack/UltronTactIQ-Mistral-2-7b" \
        --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": "CapiJack/UltronTactIQ-Mistral-2-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CapiJack/UltronTactIQ-Mistral-2-7b with Docker Model Runner:
```
docker model run hf.co/CapiJack/UltronTactIQ-Mistral-2-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

See axolotl config

axolotl version: 0.4.0

adapter: null
base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
batch_size: 2
bf16: auto
dataset_prepared_path: null
datasets:
- ds_type: json
  path: /workspace/data.jsonl
  type: context_qa.load_v2
debug: null
deepspeed: null
early_stopping_patience: null
evals_per_epoch: 4
flash_attention: null
fp16: null
fsdp: null
fsdp_config: null
gptq_groupsize: null
gptq_model_v1: null
gradient_checkpointing: true
group_by_length: false
learning_rate: 1.0e-05
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.2
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
max_packed_sequence_len: null
micro_batch_size: 1
model_config:
  output_router_logits: true
model_type: MistralForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: /workspace/out-mistral-2B
resume_from_checkpoint: null
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
  bos_token: <s>
  eos_token: <|im_end|>
  pad_token: </s>
tf32: true
tokenizer_type: LlamaTokenizer
torchdistx_path: null
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_log_model: Nous-Hermes-2-Mistral-7B-DPO
wandb_name: mistral
wandb_project: Ultron-llama
wandb_watch: null
warmup_steps: 40
weight_decay: 0.0
xformers_attention: true

workspace/out-mistral-2B

This model is a fine-tuned version of NousResearch/Nous-Hermes-2-Mistral-7B-DPO on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5036

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 2
total_train_batch_size: 4
total_eval_batch_size: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 40
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
0.6411	0.02	1	0.4803
0.5321	0.26	11	0.3867
0.4077	0.51	22	0.3591
0.4455	0.77	33	0.3995
0.2921	1.02	44	0.4368
0.3459	1.28	55	0.4884
0.2768	1.53	66	0.4978
0.4168	1.79	77	0.4808
0.14	2.05	88	0.4547
0.1132	2.3	99	0.4856
0.1055	2.56	110	0.4916
0.1385	2.81	121	0.4783
0.0455	3.07	132	0.4677
0.0211	3.33	143	0.4892
0.0236	3.58	154	0.5016
0.009	3.84	165	0.5036

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.18.0
Tokenizers 0.15.0

Downloads last month: 1

Model tree for CapiJack/UltronTactIQ-Mistral-2-7b

Base model

mistralai/Mistral-7B-v0.1

Finetuned

NousResearch/Nous-Hermes-2-Mistral-7B-DPO

Finetuned

(9)

this model

Quantizations

1 model