Text Generation
Transformers
Safetensors
English
llama
unsloth
llama-3
conversational
text-generation-inference
Instructions to use unsloth/llama-3-8b-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/llama-3-8b-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/llama-3-8b-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("unsloth/llama-3-8b-Instruct") model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct") 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
- vLLM
How to use unsloth/llama-3-8b-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/llama-3-8b-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/llama-3-8b-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/llama-3-8b-Instruct
- SGLang
How to use unsloth/llama-3-8b-Instruct 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 "unsloth/llama-3-8b-Instruct" \ --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": "unsloth/llama-3-8b-Instruct", "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 "unsloth/llama-3-8b-Instruct" \ --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": "unsloth/llama-3-8b-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use unsloth/llama-3-8b-Instruct with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/llama-3-8b-Instruct to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/llama-3-8b-Instruct to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/llama-3-8b-Instruct to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unsloth/llama-3-8b-Instruct", max_seq_length=2048, ) - Docker Model Runner
How to use unsloth/llama-3-8b-Instruct with Docker Model Runner:
docker model run hf.co/unsloth/llama-3-8b-Instruct
Upload tokenizer.json
Browse files- tokenizer.json +64 -4
tokenizer.json
CHANGED
|
@@ -2329,10 +2329,69 @@
|
|
| 2329 |
]
|
| 2330 |
},
|
| 2331 |
"post_processor": {
|
| 2332 |
-
"type": "
|
| 2333 |
-
"
|
| 2334 |
-
|
| 2335 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2336 |
},
|
| 2337 |
"decoder": {
|
| 2338 |
"type": "ByteLevel",
|
|
@@ -2348,6 +2407,7 @@
|
|
| 2348 |
"end_of_word_suffix": null,
|
| 2349 |
"fuse_unk": false,
|
| 2350 |
"byte_fallback": false,
|
|
|
|
| 2351 |
"vocab": {
|
| 2352 |
"!": 0,
|
| 2353 |
"\"": 1,
|
|
|
|
| 2329 |
]
|
| 2330 |
},
|
| 2331 |
"post_processor": {
|
| 2332 |
+
"type": "Sequence",
|
| 2333 |
+
"processors": [
|
| 2334 |
+
{
|
| 2335 |
+
"type": "ByteLevel",
|
| 2336 |
+
"add_prefix_space": true,
|
| 2337 |
+
"trim_offsets": false,
|
| 2338 |
+
"use_regex": true
|
| 2339 |
+
},
|
| 2340 |
+
{
|
| 2341 |
+
"type": "TemplateProcessing",
|
| 2342 |
+
"single": [
|
| 2343 |
+
{
|
| 2344 |
+
"SpecialToken": {
|
| 2345 |
+
"id": "<|begin_of_text|>",
|
| 2346 |
+
"type_id": 0
|
| 2347 |
+
}
|
| 2348 |
+
},
|
| 2349 |
+
{
|
| 2350 |
+
"Sequence": {
|
| 2351 |
+
"id": "A",
|
| 2352 |
+
"type_id": 0
|
| 2353 |
+
}
|
| 2354 |
+
}
|
| 2355 |
+
],
|
| 2356 |
+
"pair": [
|
| 2357 |
+
{
|
| 2358 |
+
"SpecialToken": {
|
| 2359 |
+
"id": "<|begin_of_text|>",
|
| 2360 |
+
"type_id": 0
|
| 2361 |
+
}
|
| 2362 |
+
},
|
| 2363 |
+
{
|
| 2364 |
+
"Sequence": {
|
| 2365 |
+
"id": "A",
|
| 2366 |
+
"type_id": 0
|
| 2367 |
+
}
|
| 2368 |
+
},
|
| 2369 |
+
{
|
| 2370 |
+
"SpecialToken": {
|
| 2371 |
+
"id": "<|begin_of_text|>",
|
| 2372 |
+
"type_id": 1
|
| 2373 |
+
}
|
| 2374 |
+
},
|
| 2375 |
+
{
|
| 2376 |
+
"Sequence": {
|
| 2377 |
+
"id": "B",
|
| 2378 |
+
"type_id": 1
|
| 2379 |
+
}
|
| 2380 |
+
}
|
| 2381 |
+
],
|
| 2382 |
+
"special_tokens": {
|
| 2383 |
+
"<|begin_of_text|>": {
|
| 2384 |
+
"id": "<|begin_of_text|>",
|
| 2385 |
+
"ids": [
|
| 2386 |
+
128000
|
| 2387 |
+
],
|
| 2388 |
+
"tokens": [
|
| 2389 |
+
"<|begin_of_text|>"
|
| 2390 |
+
]
|
| 2391 |
+
}
|
| 2392 |
+
}
|
| 2393 |
+
}
|
| 2394 |
+
]
|
| 2395 |
},
|
| 2396 |
"decoder": {
|
| 2397 |
"type": "ByteLevel",
|
|
|
|
| 2407 |
"end_of_word_suffix": null,
|
| 2408 |
"fuse_unk": false,
|
| 2409 |
"byte_fallback": false,
|
| 2410 |
+
"ignore_merges": true,
|
| 2411 |
"vocab": {
|
| 2412 |
"!": 0,
|
| 2413 |
"\"": 1,
|