Instructions to use shanchen/llama3-8B-slerp-biomed-chat-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shanchen/llama3-8B-slerp-biomed-chat-chinese with Transformers:

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

pipe = pipeline("text-generation", model="shanchen/llama3-8B-slerp-biomed-chat-chinese")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shanchen/llama3-8B-slerp-biomed-chat-chinese")
model = AutoModelForCausalLM.from_pretrained("shanchen/llama3-8B-slerp-biomed-chat-chinese")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shanchen/llama3-8B-slerp-biomed-chat-chinese with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shanchen/llama3-8B-slerp-biomed-chat-chinese"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shanchen/llama3-8B-slerp-biomed-chat-chinese",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shanchen/llama3-8B-slerp-biomed-chat-chinese

SGLang

How to use shanchen/llama3-8B-slerp-biomed-chat-chinese 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 "shanchen/llama3-8B-slerp-biomed-chat-chinese" \
    --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": "shanchen/llama3-8B-slerp-biomed-chat-chinese",
		"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 "shanchen/llama3-8B-slerp-biomed-chat-chinese" \
        --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": "shanchen/llama3-8B-slerp-biomed-chat-chinese",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shanchen/llama3-8B-slerp-biomed-chat-chinese with Docker Model Runner:
```
docker model run hf.co/shanchen/llama3-8B-slerp-biomed-chat-chinese
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

llama3-8B-slerp-biomed-chat-chinese

llama3-8B-slerp-biomed-chat-chinese is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
      - model: shanchen/llama3-8B-slerp-med-chinese
        layer_range: [0,32]
      - model: shenzhi-wang/Llama3-8B-Chinese-Chat
        layer_range: [0,32]
merge_method: slerp
base_model: shenzhi-wang/Llama3-8B-Chinese-Chat
parameters:
  t:
    - filter: self_attn
      value: [0.3, 0.5, 0.5, 0.7, 1]
    - filter: mlp
      value: [1, 0.7, 0.5, 0.5, 0.3]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "shanchen/llama3-8B-slerp-biomed-chat-chinese"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)

messages = [
    {"role": "user", "content": "Can you speak Japanese?"},
]

input_ids = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=192 max#8192,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for shanchen/llama3-8B-slerp-biomed-chat-chinese

shanchen/llama3-8B-slerp-med-chinese

shenzhi-wang/Llama3-8B-Chinese-Chat

Merge model

this model

Quantizations

2 models

shanchen
/

llama3-8B-slerp-biomed-chat-chinese

llama3-8B-slerp-biomed-chat-chinese

🧩 Configuration

💻 Usage

Model tree for shanchen/llama3-8B-slerp-biomed-chat-chinese

Spaces using shanchen/llama3-8B-slerp-biomed-chat-chinese 9