Instructions to use dexhrestha/mia_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexhrestha/mia_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dexhrestha/mia_model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dexhrestha/mia_model") model = AutoModelForCausalLM.from_pretrained("dexhrestha/mia_model") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use dexhrestha/mia_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dexhrestha/mia_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dexhrestha/mia_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dexhrestha/mia_model
- SGLang
How to use dexhrestha/mia_model 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 "dexhrestha/mia_model" \ --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": "dexhrestha/mia_model", "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 "dexhrestha/mia_model" \ --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": "dexhrestha/mia_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dexhrestha/mia_model with Docker Model Runner:
docker model run hf.co/dexhrestha/mia_model
Commit ·
593df5f
1
Parent(s): f4464c9
Training in progress epoch 2
Browse files- README.md +4 -3
- tf_model.h5 +1 -1
README.md
CHANGED
|
@@ -14,9 +14,9 @@ probably proofread and complete it, then remove this comment. -->
|
|
| 14 |
|
| 15 |
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
|
| 16 |
It achieves the following results on the evaluation set:
|
| 17 |
-
- Train Loss: 8.
|
| 18 |
-
- Validation Loss:
|
| 19 |
-
- Epoch:
|
| 20 |
|
| 21 |
## Model description
|
| 22 |
|
|
@@ -44,6 +44,7 @@ The following hyperparameters were used during training:
|
|
| 44 |
|:----------:|:---------------:|:-----:|
|
| 45 |
| 10.0484 | 9.0947 | 0 |
|
| 46 |
| 8.7641 | 8.3983 | 1 |
|
|
|
|
| 47 |
|
| 48 |
|
| 49 |
### Framework versions
|
|
|
|
| 14 |
|
| 15 |
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
|
| 16 |
It achieves the following results on the evaluation set:
|
| 17 |
+
- Train Loss: 8.0876
|
| 18 |
+
- Validation Loss: 7.6766
|
| 19 |
+
- Epoch: 2
|
| 20 |
|
| 21 |
## Model description
|
| 22 |
|
|
|
|
| 44 |
|:----------:|:---------------:|:-----:|
|
| 45 |
| 10.0484 | 9.0947 | 0 |
|
| 46 |
| 8.7641 | 8.3983 | 1 |
|
| 47 |
+
| 8.0876 | 7.6766 | 2 |
|
| 48 |
|
| 49 |
|
| 50 |
### Framework versions
|
tf_model.h5
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 467897424
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a691c513054f269077372b35fa11e2d82f7fca1ca3b9f758f3255dbb46fd07b9
|
| 3 |
size 467897424
|