Instructions to use tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT")
model = AutoModelForCausalLM.from_pretrained("tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT", 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 tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT

SGLang

How to use tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT 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 "tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT" \
    --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": "tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT",
		"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 "tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT" \
        --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": "tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT with Docker Model Runner:
```
docker model run hf.co/tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT
```

final_checkpoint

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Llama-8B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4805

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-06
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Use adafactor and the args are: No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss
2.6773	0.0464	50	2.4460
0.7309	0.0929	100	0.7180
0.5965	0.1393	150	0.6000
0.5401	0.1857	200	0.5535
0.5055	0.2321	250	0.5285
0.4901	0.2786	300	0.5148
0.5266	0.3250	350	0.5047
0.4829	0.3714	400	0.4973
0.4825	0.4178	450	0.4922
0.508	0.4643	500	0.4886
0.503	0.5107	550	0.4858
0.514	0.5571	600	0.4835
0.492	0.6035	650	0.4822
0.4743	0.6500	700	0.4814
0.4942	0.6964	750	0.4809
0.4811	0.7428	800	0.4806
0.4645	0.7892	850	0.4805
0.4673	0.8357	900	0.4805
0.4933	0.8821	950	0.4805
0.5026	0.9285	1000	0.4805

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.1
Tokenizers 0.21.0

Downloads last month: 10

Safetensors

Model size

8B params

Tensor type

F16

Model tree for tsavage68/Hand_off_DS_Llama8B_1000steps_1e6rate_SFT

Base model

deepseek-ai/DeepSeek-R1-Distill-Llama-8B

Finetuned

(176)

this model