Instructions to use chathuru/cicids2018-falcon7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chathuru/cicids2018-falcon7b with Transformers:

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

pipe = pipeline("text-generation", model="chathuru/cicids2018-falcon7b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chathuru/cicids2018-falcon7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("chathuru/cicids2018-falcon7b", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chathuru/cicids2018-falcon7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chathuru/cicids2018-falcon7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chathuru/cicids2018-falcon7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chathuru/cicids2018-falcon7b

SGLang

How to use chathuru/cicids2018-falcon7b 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 "chathuru/cicids2018-falcon7b" \
    --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": "chathuru/cicids2018-falcon7b",
		"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 "chathuru/cicids2018-falcon7b" \
        --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": "chathuru/cicids2018-falcon7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chathuru/cicids2018-falcon7b with Docker Model Runner:
```
docker model run hf.co/chathuru/cicids2018-falcon7b
```

chathuru commited on Dec 20, 2023

Commit

efe531d

1 Parent(s): 6755e19

Upload config

Browse files

Files changed (1) hide show

config.json +3 -16

config.json CHANGED Viewed

@@ -3,7 +3,7 @@
   "alibi": false,
   "apply_residual_connection_post_layernorm": false,
   "architectures": [
-    "FalconForSequenceClassification"
   ],
   "attention_dropout": 0.0,
   "auto_map": {
@@ -29,23 +29,10 @@
   "num_hidden_layers": 32,
   "num_kv_heads": 71,
   "parallel_attn": true,
-  "problem_type": "single_label_classification",
-  "quantization_config": {
-    "bnb_4bit_compute_dtype": "float32",
-    "bnb_4bit_quant_type": "fp4",
-    "bnb_4bit_use_double_quant": false,
-    "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": false,
-    "load_in_8bit": true,
-    "quant_method": "bitsandbytes"
-  },
   "rope_scaling": null,
   "rope_theta": 10000.0,
-  "torch_dtype": "float16",
   "transformers_version": "4.35.2",
-  "use_cache": false,
   "vocab_size": 65024
 }

   "alibi": false,
   "apply_residual_connection_post_layernorm": false,
   "architectures": [
+    "FalconForCausalLM"
   ],
   "attention_dropout": 0.0,
   "auto_map": {
   "num_hidden_layers": 32,
   "num_kv_heads": 71,
   "parallel_attn": true,
   "rope_scaling": null,
   "rope_theta": 10000.0,
+  "torch_dtype": "bfloat16",
   "transformers_version": "4.35.2",
+  "use_cache": true,
   "vocab_size": 65024
 }