Instructions to use Norquinal/Jamba-v0.1-Claude-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Norquinal/Jamba-v0.1-Claude-Chat with Transformers:

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

pipe = pipeline("text-generation", model="Norquinal/Jamba-v0.1-Claude-Chat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Norquinal/Jamba-v0.1-Claude-Chat")
model = AutoModelForCausalLM.from_pretrained("Norquinal/Jamba-v0.1-Claude-Chat", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Norquinal/Jamba-v0.1-Claude-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Norquinal/Jamba-v0.1-Claude-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Norquinal/Jamba-v0.1-Claude-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Norquinal/Jamba-v0.1-Claude-Chat

SGLang

How to use Norquinal/Jamba-v0.1-Claude-Chat 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 "Norquinal/Jamba-v0.1-Claude-Chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Norquinal/Jamba-v0.1-Claude-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Norquinal/Jamba-v0.1-Claude-Chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Norquinal/Jamba-v0.1-Claude-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Norquinal/Jamba-v0.1-Claude-Chat with Docker Model Runner:
```
docker model run hf.co/Norquinal/Jamba-v0.1-Claude-Chat
```

Jamba-v0.1-Claude-Chat / config.json

Norquinal

Upload JambaForCausalLM

dfe1865 verified over 2 years ago

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1.06 kB

	{
	"_name_or_path": "TechxGenus/Jamba-v0.1-hf",
	"architectures": [
	"JambaForCausalLM"
	],
	"attention_dropout": 0.0,
	"attn_layer_offset": 4,
	"attn_layer_period": 8,
	"bos_token_id": 1,
	"eos_token_id": 2,
	"expert_layer_offset": 1,
	"expert_layer_period": 2,
	"hidden_act": "silu",
	"hidden_size": 4096,
	"initializer_range": 0.02,
	"intermediate_size": 14336,
	"mamba_conv_bias": true,
	"mamba_d_conv": 4,
	"mamba_d_state": 16,
	"mamba_dt_rank": 256,
	"mamba_expand": 2,
	"mamba_proj_bias": false,
	"max_position_embeddings": 262144,
	"model_type": "jamba",
	"num_attention_heads": 32,
	"num_experts": 16,
	"num_experts_per_tok": 2,
	"num_hidden_layers": 32,
	"num_key_value_heads": 8,
	"num_logits_to_keep": 1,
	"output_router_logits": false,
	"pad_token_id": 0,
	"rms_norm_eps": 1e-06,
	"router_aux_loss_coef": 0.001,
	"sliding_window": null,
	"tie_word_embeddings": false,
	"torch_dtype": "float16",
	"transformers_version": "4.40.1",
	"use_cache": true,
	"use_mamba_kernels": true,
	"vocab_size": 65536
	}