Text Generation
Transformers
Safetensors
English
llama
rl-mpq
mixed-precision
quantization
fake-quantization
llama-2
text-generation-inference
Instructions to use AvoCahDoe/llama-2-7b-rlmpq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AvoCahDoe/llama-2-7b-rlmpq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AvoCahDoe/llama-2-7b-rlmpq")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AvoCahDoe/llama-2-7b-rlmpq") model = AutoModelForCausalLM.from_pretrained("AvoCahDoe/llama-2-7b-rlmpq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AvoCahDoe/llama-2-7b-rlmpq with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AvoCahDoe/llama-2-7b-rlmpq" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AvoCahDoe/llama-2-7b-rlmpq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AvoCahDoe/llama-2-7b-rlmpq
- SGLang
How to use AvoCahDoe/llama-2-7b-rlmpq 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 "AvoCahDoe/llama-2-7b-rlmpq" \ --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": "AvoCahDoe/llama-2-7b-rlmpq", "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 "AvoCahDoe/llama-2-7b-rlmpq" \ --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": "AvoCahDoe/llama-2-7b-rlmpq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AvoCahDoe/llama-2-7b-rlmpq with Docker Model Runner:
docker model run hf.co/AvoCahDoe/llama-2-7b-rlmpq
RL-MPQ High_Fidelity — 2026-06-11T15:29:08.676886
Browse files- High_Fidelity/README.md +45 -0
- High_Fidelity/config.json +33 -0
- High_Fidelity/generation_config.json +10 -0
- High_Fidelity/model.safetensors +3 -0
- High_Fidelity/rlmpq_policy.json +60 -0
- High_Fidelity/tokenizer.json +0 -0
- High_Fidelity/tokenizer_config.json +16 -0
High_Fidelity/README.md
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---
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license: llama2
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base_model: meta-llama/Llama-2-7b-hf
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tags:
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- rl-mpq
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- mixed-precision
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- quantization
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library_name: transformers
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---
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# LLAMA-2-7B — High Fidelity
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Subfolder of [`AvoCahDoe/llama-2-7b-rlmpq`](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq) (all RL-MPQ scenarios for this model).
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Fake-quantized weights for **High_Fidelity** applied to
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[meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) (per-layer asymmetric group-wise quant, group_size=128).
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## Load
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "AvoCahDoe/llama-2-7b-rlmpq"
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model = AutoModelForCausalLM.from_pretrained(repo, subfolder="High_Fidelity", torch_dtype="float16")
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tokenizer = AutoTokenizer.from_pretrained(repo, subfolder="High_Fidelity")
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```
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## Metrics
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| Metric | Value |
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|--------|-------|
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| Scenario | High_Fidelity |
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| Average bits per weight | 6.5 |
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| Compression ratio (vs FP16) | 2.4615x |
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| WikiText-2 perplexity | 4.9808 |
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| Layers | 32 |
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| Bit distribution | {'4': 12, '8': 20} |
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## Policy (bits per layer)
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```
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[8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]
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```
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Generated: 2026-06-11T15:29:08.289209
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High_Fidelity/config.json
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{
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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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"dtype": "float16",
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"eos_token_id": 2,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 4096,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default",
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"type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.11.0",
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"use_cache": true,
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"vocab_size": 32000
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}
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High_Fidelity/generation_config.json
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{
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"bos_token_id": 1,
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"do_sample": true,
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"eos_token_id": 2,
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"max_length": 4096,
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"pad_token_id": 0,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "5.11.0"
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}
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High_Fidelity/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:dae9da6880e3eac25d0ba6965603254cc70c9199def508ec9d49334af21fdcd1
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size 13476864944
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High_Fidelity/rlmpq_policy.json
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{
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"slug": "llama_2_7b",
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"hf_id": "meta-llama/Llama-2-7b-hf",
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"scenario": "High_Fidelity",
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"params": {
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"lambda_mse": 1.0,
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"lambda_bit": 0.05,
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"max_mse_clip": 1.0,
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"target_bits": 7.0,
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"lambda_budget": 2.0
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},
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"policy": [
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8,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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4,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8
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],
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"num_layers": 32,
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"avg_bits": 6.5,
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"total_reward": -1.919881,
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"compression_ratio": 2.4615,
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| 50 |
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"approx_size_gb": 5.688,
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"bit_distribution": {
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"4": 12,
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"8": 20
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},
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"avg_mse_per_bit": {
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"8": 0.00020028,
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"4": 0.36544608
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},
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"validation_elapsed_s": 0.0169
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}
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High_Fidelity/tokenizer.json
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High_Fidelity/tokenizer_config.json
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{
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"add_prefix_space": null,
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"backend": "tokenizers",
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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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"is_local": false,
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| 8 |
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"local_files_only": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": null,
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"padding_side": "right",
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"sp_model_kwargs": {},
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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