Instructions to use W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64 with Transformers:

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

pipe = pipeline("text-generation", model="W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64")
model = AutoModelForCausalLM.from_pretrained("W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64", 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 W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64

SGLang

How to use W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64 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 "W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64" \
    --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": "W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64",
		"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 "W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64" \
        --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": "W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64 with Docker Model Runner:
```
docker model run hf.co/W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64
```

mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260418-015332

This model is a fine-tuned version of mistral-7b-base-sft-hh-helpful-4xh200-batch-64-20260418-015332 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

Loss: 0.6015
Beta Dpo/beta: 0.0010
Beta Dpo/loss Margin Mean: 243.4043
Beta Dpo/beta Margin Mean: 0.2434
Beta Dpo/beta Margin Std: 0.4217
Beta Dpo/beta Margin Grad Mean: -0.4422
Beta Dpo/beta Margin Grad Std: 0.0983
Beta Dpo/gap Mean: 404.4037
Beta Dpo/gap Std: 357.4069
Beta Dpo/beta Used Raw: -9.5600
Beta Dpo/beta Used: 0.0010
Beta Dpo/mask Keep Frac: 1.0
Logits/chosen: -2.7813
Logits/rejected: -2.8108

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: 5e-07
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Beta Dpo/beta	Beta Dpo/loss Margin Mean	Beta Dpo/beta Margin Mean	Beta Dpo/beta Margin Std	Beta Dpo/beta Margin Grad Mean	Beta Dpo/beta Margin Grad Std	Beta Dpo/gap Mean	Beta Dpo/gap Std	Beta Dpo/beta Used Raw	Beta Dpo/beta Used	Beta Dpo/mask Keep Frac	Logits/chosen	Logits/rejected
1.3346	0.1468	100	0.7825	0.0211	38.6966	1.4685	2.0475	-0.4727	0.0403	60.6513	63.8526	-1.2173	0.0211	1.0	-2.9129	-2.9033
1.265	0.2937	200	1.2116	0.0416	108.9061	8.0746	10.4591	-0.4594	0.0608	175.9197	183.7102	-3.9208	0.0416	1.0	-2.3116	-2.3059
0.5857	0.4405	300	0.6708	0.0032	165.3890	0.8039	1.0106	-0.4553	0.0715	284.4015	265.4041	-7.0408	0.0032	1.0	-2.3951	-2.3756
3.7878	0.5874	400	0.6122	0.0010	205.4126	0.2054	0.3571	-0.4506	0.0845	362.1024	333.2912	-9.3014	0.0010	1.0	-2.4431	-2.4332
6.7444	0.7342	500	0.6026	0.0010	233.9227	0.2339	0.3910	-0.4441	0.0919	390.5113	345.8571	-9.2953	0.0010	1.0	-2.6421	-2.6564
0.5388	0.8811	600	0.6015	0.0010	243.4043	0.2434	0.4217	-0.4422	0.0983	404.4037	357.4069	-9.5600	0.0010	1.0	-2.7813	-2.8108

Framework versions

Transformers 4.51.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.21.4

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

F32

W-61
/

mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64

mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260418-015332

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train W-61/mistral-7b-base-beta-dpo-hh-helpful-4xh200-batch-64