Instructions to use jakiAJK/internlm3-8b-instruct_GPTQ-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jakiAJK/internlm3-8b-instruct_GPTQ-int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jakiAJK/internlm3-8b-instruct_GPTQ-int4", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("jakiAJK/internlm3-8b-instruct_GPTQ-int4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jakiAJK/internlm3-8b-instruct_GPTQ-int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jakiAJK/internlm3-8b-instruct_GPTQ-int4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jakiAJK/internlm3-8b-instruct_GPTQ-int4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jakiAJK/internlm3-8b-instruct_GPTQ-int4
- SGLang
How to use jakiAJK/internlm3-8b-instruct_GPTQ-int4 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 "jakiAJK/internlm3-8b-instruct_GPTQ-int4" \ --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": "jakiAJK/internlm3-8b-instruct_GPTQ-int4", "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 "jakiAJK/internlm3-8b-instruct_GPTQ-int4" \ --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": "jakiAJK/internlm3-8b-instruct_GPTQ-int4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jakiAJK/internlm3-8b-instruct_GPTQ-int4 with Docker Model Runner:
docker model run hf.co/jakiAJK/internlm3-8b-instruct_GPTQ-int4
| { | |
| "_name_or_path": "./tmp_autoround_gptq", | |
| "architectures": [ | |
| "InternLM3ForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_internlm3.InternLM3Config", | |
| "AutoModel": "modeling_internlm3.InternLM3Model", | |
| "AutoModelForCausalLM": "modeling_internlm3.InternLM3ForCausalLM" | |
| }, | |
| "bias": false, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 10240, | |
| "max_position_embeddings": 32768, | |
| "model_type": "internlm3", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 48, | |
| "num_key_value_heads": 2, | |
| "pad_token_id": 2, | |
| "qkv_bias": false, | |
| "quantization_config": { | |
| "batch_size": 4, | |
| "bits": 4, | |
| "block_name_to_quantize": null, | |
| "cache_block_outputs": true, | |
| "damp_percent": 0.01, | |
| "dataset": null, | |
| "desc_act": false, | |
| "exllama_config": { | |
| "version": 1 | |
| }, | |
| "group_size": 128, | |
| "max_input_length": null, | |
| "model_seqlen": null, | |
| "module_name_preceding_first_block": null, | |
| "modules_in_block_to_quantize": null, | |
| "pad_token_id": null, | |
| "quant_method": "gptq", | |
| "sym": true, | |
| "tokenizer": null, | |
| "true_sequential": false, | |
| "use_cuda_fp16": false, | |
| "use_exllama": true | |
| }, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "factor": 6.0, | |
| "rope_type": "dynamic" | |
| }, | |
| "rope_theta": 50000000, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.48.0", | |
| "use_cache": true, | |
| "vocab_size": 128512 | |
| } | |