Text Generation
Transformers
Safetensors
llama
conversational
text-generation-inference
8-bit precision
compressed-tensors
Instructions to use nm-testing/tinyllama-one-shot-static-quant-test-compressed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nm-testing/tinyllama-one-shot-static-quant-test-compressed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nm-testing/tinyllama-one-shot-static-quant-test-compressed") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nm-testing/tinyllama-one-shot-static-quant-test-compressed") model = AutoModelForCausalLM.from_pretrained("nm-testing/tinyllama-one-shot-static-quant-test-compressed") 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
- vLLM
How to use nm-testing/tinyllama-one-shot-static-quant-test-compressed with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nm-testing/tinyllama-one-shot-static-quant-test-compressed" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nm-testing/tinyllama-one-shot-static-quant-test-compressed", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nm-testing/tinyllama-one-shot-static-quant-test-compressed
- SGLang
How to use nm-testing/tinyllama-one-shot-static-quant-test-compressed 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 "nm-testing/tinyllama-one-shot-static-quant-test-compressed" \ --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": "nm-testing/tinyllama-one-shot-static-quant-test-compressed", "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 "nm-testing/tinyllama-one-shot-static-quant-test-compressed" \ --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": "nm-testing/tinyllama-one-shot-static-quant-test-compressed", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nm-testing/tinyllama-one-shot-static-quant-test-compressed with Docker Model Runner:
docker model run hf.co/nm-testing/tinyllama-one-shot-static-quant-test-compressed
Upload folder using huggingface_hub
Browse files- config.json +51 -43
- generation_config.json +1 -1
- model.safetensors +2 -2
- recipe.yaml +3 -14
- tokenizer_config.json +1 -0
config.json
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{
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"_name_or_path": "./
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"architectures": [
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"LlamaForCausalLM"
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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": 2048,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"quantization_config": {
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"config_groups": {
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"group_0": {
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"input_activations": {
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"block_structure": null,
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"dynamic": false,
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"group_size": null,
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"num_bits": 8,
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"observer": "minmax",
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"observer_kwargs": {},
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"strategy": "tensor",
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"symmetric": true,
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"type": "int"
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},
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"output_activations": null,
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"targets": [
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"Linear"
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],
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"weights": {
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"block_structure": null,
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"dynamic": false,
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"group_size": null,
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"num_bits": 8,
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"observer": "minmax",
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"observer_kwargs": {},
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"strategy": "tensor",
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"symmetric": true,
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"type": "int"
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}
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}
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},
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"format": "compressed",
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"global_compression_ratio": 1.1282749751199026,
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"ignore": [
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"model.layers.0.mlp.down_proj",
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"lm_head"
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],
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"quant_method": "sparseml",
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"quantization_status": "compressed"
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},
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"tie_word_embeddings": false,
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"torch_dtype": "
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"transformers_version": "4.39.
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"use_cache": true,
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"vocab_size": 32000
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}
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{
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"_name_or_path": "/root/.cache/huggingface/hub/models--TinyLlama--TinyLlama-1.1B-intermediate-step-1431k-3T/snapshots/036fa4651240b9a1487f709833b9e4b96b4c1574",
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"architectures": [
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"LlamaForCausalLM"
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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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"compression_config": {
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"quantization_config": {
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"config_groups": {
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"group_0": {
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"input_activations": {
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"block_structure": null,
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"dynamic": false,
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"group_size": null,
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"num_bits": 8,
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"observer": "minmax",
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"observer_kwargs": {},
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"strategy": "tensor",
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"symmetric": true,
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"type": "int"
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},
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"output_activations": null,
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"targets": [
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"Linear"
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],
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"weights": {
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"block_structure": null,
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"dynamic": false,
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"group_size": null,
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"num_bits": 8,
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"observer": "minmax",
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"observer_kwargs": {},
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"strategy": "tensor",
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"symmetric": true,
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"type": "int"
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}
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}
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},
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"format": "int-quantized",
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"global_compression_ratio": 1.454005842904474,
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"ignore": [
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"model.layers.0.mlp.down_proj",
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"lm_head"
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],
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"quant_method": "sparseml",
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"quantization_status": "frozen"
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},
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"sparsity_config": {
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"format": "dense",
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"global_sparsity": 7.713156824200198,
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"registry_requires_subclass": false,
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"sparsity_structure": "0:0"
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}
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},
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.0",
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"use_cache": true,
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"vocab_size": 32000
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}
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generation_config.json
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"eos_token_id": 2,
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"max_length": 2048,
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"pad_token_id": 0,
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"transformers_version": "4.39.
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}
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"eos_token_id": 2,
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"max_length": 2048,
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"pad_token_id": 0,
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"transformers_version": "4.39.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:2904d1e37d759d31bbdec6c0b5c226d8ca1db1bc487dd69eb3f49c75ecdcb640
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size 1242839118
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recipe.yaml
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quant_modifiers:
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vLLMQuantizationModifier:
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ignore: [lm_head, model.layers.0.mlp.down_proj]
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config_groups:
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group_0:
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weights: {num_bits: 8, type: int, symmetric: true, strategy: tensor}
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input_activations: {num_bits: 8, type: int, symmetric:
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output_activations: null
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targets: [Linear]
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weights: {num_bits: 8, type: int, symmetric: true, strategy: tensor}
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input_activations: null
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output_activations: null
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targets: [Embedding]
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SparseGPTModifier:
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sparsity: 0.0
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block_size: 128
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sequential_update: false
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quantize: true
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targets: ['re:model.layers.\d+$']
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quant_stage:
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quant_modifiers:
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vLLMQuantizationModifier:
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ignore: [lm_head, model.layers.0.mlp.down_proj]
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config_groups:
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group_0:
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weights: {num_bits: 8, type: int, symmetric: true, strategy: tensor}
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input_activations: {num_bits: 8, type: int, symmetric: true, strategy: tensor}
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targets: [Linear]
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SparseGPTModifier: {sparsity: 0.0, quantize: true, sequential_update: false}
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tokenizer_config.json
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}
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},
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": false,
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}
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},
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"bos_token": "<s>",
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"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\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": false,
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