Text Generation
Transformers
Safetensors
English
mixtral
Mixture of Experts
conversational
text-generation-inference
4-bit precision
awq
Instructions to use TheBloke/FusionNet_34Bx2_MoE-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/FusionNet_34Bx2_MoE-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/FusionNet_34Bx2_MoE-AWQ") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/FusionNet_34Bx2_MoE-AWQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/FusionNet_34Bx2_MoE-AWQ") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheBloke/FusionNet_34Bx2_MoE-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/FusionNet_34Bx2_MoE-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/FusionNet_34Bx2_MoE-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/TheBloke/FusionNet_34Bx2_MoE-AWQ
- SGLang
How to use TheBloke/FusionNet_34Bx2_MoE-AWQ 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 "TheBloke/FusionNet_34Bx2_MoE-AWQ" \ --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": "TheBloke/FusionNet_34Bx2_MoE-AWQ", "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 "TheBloke/FusionNet_34Bx2_MoE-AWQ" \ --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": "TheBloke/FusionNet_34Bx2_MoE-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use TheBloke/FusionNet_34Bx2_MoE-AWQ with Docker Model Runner:
docker model run hf.co/TheBloke/FusionNet_34Bx2_MoE-AWQ
AWQ model commit
Browse files- config.json +44 -0
- generation_config.json +7 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +0 -0
- quant_config.json +9 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +42 -0
config.json
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{
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"_name_or_path": "/workspace/process/tomgrc_fusionnet_34bx2_moe/source",
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"architectures": [
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"MixtralForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 7168,
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"initializer_range": 0.02,
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"intermediate_size": 20480,
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"max_position_embeddings": 32768,
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"model_type": "mixtral",
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"num_attention_heads": 56,
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"num_experts_per_tok": 2,
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"num_hidden_layers": 60,
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"num_key_value_heads": 8,
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"num_local_experts": 2,
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"output_router_logits": false,
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"pad_token_id": 1,
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"pretraining_tp": 1,
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"quantization_config": {
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"bits": 4,
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"group_size": 128,
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"modules_to_not_convert": [
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"gate"
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],
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"quant_method": "awq",
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"version": "gemm",
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"zero_point": true
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},
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 5000000.0,
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"router_aux_loss_coef": 0.001,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.37.0.dev0",
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"use_cache": true,
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"vocab_size": 64000
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 1,
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"transformers_version": "4.37.0.dev0"
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}
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model-00001-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d207b376adec365e8912768733efbb42cf2a879a8e192b3091a509935a3b8c00
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size 9948652952
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model-00002-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:358c67b8608ba62b5b25803ce1c7a8f2f86b49879bfe40992e6403764e981e10
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size 9931198176
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model-00003-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fd548fbddd60a7f0daf55c120f69936e233c8296f382fe76b4020dbea21d512a
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size 9931198176
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model-00004-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:eb47d19fb3705b7283cb882c232da8047d5c7d3f844a0909bdbad59f2a910f54
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size 3144805376
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model.safetensors.index.json
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quant_config.json
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{
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"zero_point": true,
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"q_group_size": 128,
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"w_bit": 4,
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"version": "GEMM",
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"modules_to_not_convert": [
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"gate"
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]
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}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<s>",
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:386c49cf943d71aa110361135338c50e38beeff0a66593480421f37b319e1a39
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size 1033105
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tokenizer_config.json
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "<s>",
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"chat_template": "{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' ' + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length": 32768,
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"pad_token": "<s>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": true
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| 42 |
+
}
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