Text Generation
Transformers
Safetensors
kambo
dynquant
quantized
3-bit
text-to-sql
code
mixture-of-experts
Mixture of Experts
hybrid-architecture
conversational
custom_code
Instructions to use VikramPal/kambo-v1-sql-code-DynQuant-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VikramPal/kambo-v1-sql-code-DynQuant-3bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="VikramPal/kambo-v1-sql-code-DynQuant-3bit", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("VikramPal/kambo-v1-sql-code-DynQuant-3bit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use VikramPal/kambo-v1-sql-code-DynQuant-3bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VikramPal/kambo-v1-sql-code-DynQuant-3bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VikramPal/kambo-v1-sql-code-DynQuant-3bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/VikramPal/kambo-v1-sql-code-DynQuant-3bit
- SGLang
How to use VikramPal/kambo-v1-sql-code-DynQuant-3bit 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 "VikramPal/kambo-v1-sql-code-DynQuant-3bit" \ --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": "VikramPal/kambo-v1-sql-code-DynQuant-3bit", "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 "VikramPal/kambo-v1-sql-code-DynQuant-3bit" \ --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": "VikramPal/kambo-v1-sql-code-DynQuant-3bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use VikramPal/kambo-v1-sql-code-DynQuant-3bit with Docker Model Runner:
docker model run hf.co/VikramPal/kambo-v1-sql-code-DynQuant-3bit
Download config.json from VikramPal/kambo-v1-sql-code-DynQuant-3bit: direct link, hf CLI and curl.
- Browser
- Download file 20.4 kB
-
https://huggingface.co/VikramPal/kambo-v1-sql-code-DynQuant-3bit/resolve/main/config.json
- Command line
-
hf download hf://VikramPal/kambo-v1-sql-code-DynQuant-3bit/config.json
-
curl -L -o config.json https://huggingface.co/VikramPal/kambo-v1-sql-code-DynQuant-3bit/resolve/main/config.json
20.4 kB
| { | |
| "architectures": [ | |
| "KamboForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_kambo.KamboConfig", | |
| "AutoModel": "modeling_kambo.KamboModel", | |
| "AutoModelForCausalLM": "modeling_kambo.KamboForCausalLM" | |
| }, | |
| "bos_token_id": 151643, | |
| "conv_kernel": 3, | |
| "d_ff": 1152, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 151645, | |
| "gqa_layers": [ | |
| 3, | |
| 7, | |
| 11, | |
| 15, | |
| 19, | |
| 23 | |
| ], | |
| "head_dim": 64, | |
| "hidden_size": 1024, | |
| "intermediate_size": 1152, | |
| "max_position_embeddings": 16384, | |
| "model_type": "kambo", | |
| "n_experts": 16, | |
| "num_attention_heads": 16, | |
| "num_experts": 16, | |
| "num_experts_per_tok": 2, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 4, | |
| "pad_token_id": 151643, | |
| "quantization_config": { | |
| "checkpoint_format": "dynquant-packed", | |
| "group_size": 128, | |
| "lm_head_quantized": false, | |
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