Instructions to use second-state/internlm2_5-7b-chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/internlm2_5-7b-chat-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="second-state/internlm2_5-7b-chat-GGUF", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("second-state/internlm2_5-7b-chat-GGUF", trust_remote_code=True, dtype="auto")

llama-cpp-python

How to use second-state/internlm2_5-7b-chat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/internlm2_5-7b-chat-GGUF",
	filename="internlm2_5-7b-chat-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use second-state/internlm2_5-7b-chat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/internlm2_5-7b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/internlm2_5-7b-chat-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/internlm2_5-7b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/internlm2_5-7b-chat-GGUF:Q4_K_M

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 second-state/internlm2_5-7b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/internlm2_5-7b-chat-GGUF:Q4_K_M

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 second-state/internlm2_5-7b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/internlm2_5-7b-chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/internlm2_5-7b-chat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/internlm2_5-7b-chat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/internlm2_5-7b-chat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "second-state/internlm2_5-7b-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/internlm2_5-7b-chat-GGUF:Q4_K_M

SGLang

How to use second-state/internlm2_5-7b-chat-GGUF 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 "second-state/internlm2_5-7b-chat-GGUF" \
    --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": "second-state/internlm2_5-7b-chat-GGUF",
		"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 "second-state/internlm2_5-7b-chat-GGUF" \
        --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": "second-state/internlm2_5-7b-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use second-state/internlm2_5-7b-chat-GGUF with Ollama:
```
ollama run hf.co/second-state/internlm2_5-7b-chat-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/internlm2_5-7b-chat-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/internlm2_5-7b-chat-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/internlm2_5-7b-chat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/internlm2_5-7b-chat-GGUF to start chatting

Docker Model Runner
How to use second-state/internlm2_5-7b-chat-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/internlm2_5-7b-chat-GGUF:Q4_K_M
```

Lemonade

How to use second-state/internlm2_5-7b-chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/internlm2_5-7b-chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.internlm2_5-7b-chat-GGUF-Q4_K_M

List all available models

lemonade list

apepkuss79 commited on Jul 5, 2024

Commit

805205c

verified ·

1 Parent(s): a7e3ff5

Upload config.json with huggingface_hub

Browse files

Files changed (1) hide show

config.json +36 -0

config.json ADDED Viewed

	@@ -0,0 +1,36 @@

+{
+  "architectures": [
+    "InternLM2ForCausalLM"
+  ],
+  "attn_implementation": "eager",
+  "auto_map": {
+    "AutoConfig": "configuration_internlm2.InternLM2Config",
+    "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM",
+    "AutoModel": "modeling_internlm2.InternLM2ForCausalLM"
+  },
+  "bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "internlm2",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "type": "dynamic",
+    "factor": 2.0
+  },
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.41.0",
+  "use_cache": true,
+  "vocab_size": 92544,
+  "pretraining_tp": 1
+}