PEFT
Safetensors
GGUF
internlm3
axolotl
Generated from Trainer
custom_code
4-bit precision
bitsandbytes
conversational
Instructions to use ToastyPigeon/intern-rp-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ToastyPigeon/intern-rp-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("internlm/internlm3-8b-instruct") model = PeftModel.from_pretrained(base_model, "ToastyPigeon/intern-rp-lora") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ToastyPigeon/intern-rp-lora with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ToastyPigeon/intern-rp-lora:Q8_0 # Run inference directly in the terminal: llama cli -hf ToastyPigeon/intern-rp-lora:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ToastyPigeon/intern-rp-lora:Q8_0 # Run inference directly in the terminal: llama cli -hf ToastyPigeon/intern-rp-lora:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ToastyPigeon/intern-rp-lora:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf ToastyPigeon/intern-rp-lora:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf ToastyPigeon/intern-rp-lora:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ToastyPigeon/intern-rp-lora:Q8_0
Use Docker
docker model run hf.co/ToastyPigeon/intern-rp-lora:Q8_0
- LM Studio
- Jan
- Ollama
How to use ToastyPigeon/intern-rp-lora with Ollama:
ollama run hf.co/ToastyPigeon/intern-rp-lora:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use ToastyPigeon/intern-rp-lora with Docker Model Runner:
docker model run hf.co/ToastyPigeon/intern-rp-lora:Q8_0
- Lemonade
How to use ToastyPigeon/intern-rp-lora with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ToastyPigeon/intern-rp-lora:Q8_0
Run and chat with the model
lemonade run user.intern-rp-lora-Q8_0
List all available models
lemonade list
- Atomic Chat
File size: 1,603 Bytes
af115cd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | {
"_attn_implementation_autoset": true,
"_name_or_path": "internlm/internlm3-8b-instruct",
"architectures": [
"InternLM3ForCausalLM"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "internlm/internlm3-8b-instruct--configuration_internlm3.InternLM3Config",
"AutoModel": "internlm/internlm3-8b-instruct--modeling_internlm3.InternLM3Model",
"AutoModelForCausalLM": "internlm/internlm3-8b-instruct--modeling_internlm3.InternLM3ForCausalLM"
},
"bias": false,
"bos_token_id": 1,
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 10240,
"max_position_embeddings": 32768,
"model_type": "internlm3",
"num_attention_heads": 32,
"num_hidden_layers": 48,
"num_key_value_heads": 2,
"pad_token_id": 2,
"qkv_bias": false,
"quantization_config": {
"_load_in_4bit": true,
"_load_in_8bit": false,
"bnb_4bit_compute_dtype": "bfloat16",
"bnb_4bit_quant_storage": "bfloat16",
"bnb_4bit_quant_type": "nf4",
"bnb_4bit_use_double_quant": true,
"llm_int8_enable_fp32_cpu_offload": false,
"llm_int8_has_fp16_weight": false,
"llm_int8_skip_modules": null,
"llm_int8_threshold": 6.0,
"load_in_4bit": true,
"load_in_8bit": false,
"quant_method": "bitsandbytes"
},
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 6.0,
"rope_type": "dynamic"
},
"rope_theta": 50000000,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.47.1",
"use_cache": false,
"vocab_size": 128133
}
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