Instructions to use tiiuae/Falcon3-Mamba-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiiuae/Falcon3-Mamba-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="tiiuae/Falcon3-Mamba-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-Mamba-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-Mamba-7B-Instruct", device_map="auto")
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 Settings

vLLM

How to use tiiuae/Falcon3-Mamba-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiiuae/Falcon3-Mamba-7B-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": "tiiuae/Falcon3-Mamba-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiiuae/Falcon3-Mamba-7B-Instruct

SGLang

How to use tiiuae/Falcon3-Mamba-7B-Instruct 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 "tiiuae/Falcon3-Mamba-7B-Instruct" \
    --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": "tiiuae/Falcon3-Mamba-7B-Instruct",
		"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 "tiiuae/Falcon3-Mamba-7B-Instruct" \
        --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": "tiiuae/Falcon3-Mamba-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiiuae/Falcon3-Mamba-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/tiiuae/Falcon3-Mamba-7B-Instruct
```

Update README.md

by DhiyaEddine - opened Dec 17, 2024

base: refs/heads/main

←

from: refs/pr/8

Discussion Files changed

-8

Files changed (1) hide show

README.md +8 -8

README.md CHANGED Viewed

@@ -105,7 +105,7 @@ We report in the following table our internal pipeline benchmarks. For the bench
         <tr>
             <td rowspan="3">General</td>
             <td>MMLU (5-shot)</td>
-            <td>-</td>
             <td>68.7%</td>
             <td>55.9%</td>
             <td>65.3%</td>
@@ -127,14 +127,14 @@ We report in the following table our internal pipeline benchmarks. For the bench
         <tr>
             <td rowspan="2">Math</td>
             <td>GSM8K (5-shot)</td>
-            <td>-</td>
             <td>74.9%</td>
             <td>19.2%</td>
             <td>65.2%</td>
         </tr>
         <tr>
             <td>MATH Lvl-5 (4-shot)</td>
-            <td>-</td>
             <td>6.9%</td>
             <td>10.4%</td>
             <td>27.3%</td>
@@ -142,7 +142,7 @@ We report in the following table our internal pipeline benchmarks. For the bench
         <tr>
             <td rowspan="4">Reasoning</td>
             <td>Arc Challenge (25-shot)</td>
-            <td>-</td>
             <td>54.3%</td>
             <td>46.6%</td>
             <td>53.7%</td>
@@ -171,28 +171,28 @@ We report in the following table our internal pipeline benchmarks. For the bench
         <tr>
             <td rowspan="4">CommonSense Understanding</td>
             <td>PIQA (0-shot)</td>
-            <td>-</td>
             <td>82.3%</td>
             <td>78.9%</td>
             <td>80.9%</td>
         </tr>
         <tr>
             <td>SciQ (0-shot)</td>
-            <td>-</td>
             <td>94.9%</td>
             <td>80.2%</td>
             <td>93.6%</td>
         </tr>
         <tr>
             <td>Winogrande (0-shot)</td>
-            <td>-</td>
             <td>64.5%</td>
             <td>-</td>
             <td>-</td>
         </tr>
         <tr>
             <td>OpenbookQA (0-shot)</td>
-            <td>-</td>
             <td>34.6%</td>
             <td>46.2%</td>
             <td>47.2%</td>

         <tr>
             <td rowspan="3">General</td>
             <td>MMLU (5-shot)</td>
+            <td>30.6%</td>
             <td>68.7%</td>
             <td>55.9%</td>
             <td>65.3%</td>
         <tr>
             <td rowspan="2">Math</td>
             <td>GSM8K (5-shot)</td>
+            <td>0%</td>
             <td>74.9%</td>
             <td>19.2%</td>
             <td>65.2%</td>
         </tr>
         <tr>
             <td>MATH Lvl-5 (4-shot)</td>
+            <td>13.6%</td>
             <td>6.9%</td>
             <td>10.4%</td>
             <td>27.3%</td>
         <tr>
             <td rowspan="4">Reasoning</td>
             <td>Arc Challenge (25-shot)</td>
+            <td>54%</td>
             <td>54.3%</td>
             <td>46.6%</td>
             <td>53.7%</td>
         <tr>
             <td rowspan="4">CommonSense Understanding</td>
             <td>PIQA (0-shot)</td>
+            <td>75.6%</td>
             <td>82.3%</td>
             <td>78.9%</td>
             <td>80.9%</td>
         </tr>
         <tr>
             <td>SciQ (0-shot)</td>
+            <td>29.2%</td>
             <td>94.9%</td>
             <td>80.2%</td>
             <td>93.6%</td>
         </tr>
         <tr>
             <td>Winogrande (0-shot)</td>
+            <td>75.9%</td>
             <td>64.5%</td>
             <td>-</td>
             <td>-</td>
         </tr>
         <tr>
             <td>OpenbookQA (0-shot)</td>
+            <td>45.6%</td>
             <td>34.6%</td>
             <td>46.2%</td>
             <td>47.2%</td>