Instructions to use aethertp/PicoLM-V3-Pro-82M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use aethertp/PicoLM-V3-Pro-82M-Instruct 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 aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aethertp/PicoLM-V3-Pro-82M-Instruct: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 aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aethertp/PicoLM-V3-Pro-82M-Instruct: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 aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M

Use Docker

docker model run hf.co/aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use aethertp/PicoLM-V3-Pro-82M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aethertp/PicoLM-V3-Pro-82M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aethertp/PicoLM-V3-Pro-82M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M

Ollama
How to use aethertp/PicoLM-V3-Pro-82M-Instruct with Ollama:
```
ollama run hf.co/aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use aethertp/PicoLM-V3-Pro-82M-Instruct with Docker Model Runner:
```
docker model run hf.co/aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M
```

Lemonade

How to use aethertp/PicoLM-V3-Pro-82M-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aethertp/PicoLM-V3-Pro-82M-Instruct:Q4_K_M

Run and chat with the model

lemonade run user.PicoLM-V3-Pro-82M-Instruct-Q4_K_M

List all available models

lemonade list

Atomic Chat

aethertp commited on 22 days ago

Commit

7573418

verified ·

1 Parent(s): cf189a0

Upload modeling_picolm_v3.py with huggingface_hub

Browse files

Files changed (1) hide show

modeling_picolm_v3.py +1 -2

modeling_picolm_v3.py CHANGED Viewed

@@ -98,6 +98,7 @@ class TransformerBlockV3(nn.Module):
 class PicoLMV3ForCausalLM(PreTrainedModel, GenerationMixin):
     config_class = PicoLMV3Config
     base_model_prefix = "model"
     def __init__(self, config):
         super().__init__(config)
@@ -117,7 +118,6 @@ class PicoLMV3ForCausalLM(PreTrainedModel, GenerationMixin):
     def set_input_embeddings(self, value):
         self.tok_embeddings = value
-    # attention_mask parametresi açıkça eklendi
     def forward(self, input_ids=None, attention_mask=None, labels=None, past_key_values=None, **kwargs):
         B, S = input_ids.shape
         x_low = self.tok_embeddings(input_ids)
@@ -138,7 +138,6 @@ class PicoLMV3ForCausalLM(PreTrainedModel, GenerationMixin):
         return CausalLMOutputWithPast(loss=loss, logits=logits)
-    # prepare_inputs_for_generation attention_mask'ı artık yakalıyor
     def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **kwargs):
         model_inputs = {"input_ids": input_ids}
         if attention_mask is not None:

 class PicoLMV3ForCausalLM(PreTrainedModel, GenerationMixin):
     config_class = PicoLMV3Config
     base_model_prefix = "model"
+    _supports_cache_class = False
     def __init__(self, config):
         super().__init__(config)
     def set_input_embeddings(self, value):
         self.tok_embeddings = value
     def forward(self, input_ids=None, attention_mask=None, labels=None, past_key_values=None, **kwargs):
         B, S = input_ids.shape
         x_low = self.tok_embeddings(input_ids)
         return CausalLMOutputWithPast(loss=loss, logits=logits)
     def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **kwargs):
         model_inputs = {"input_ids": input_ids}
         if attention_mask is not None: