Text Generation
Transformers
Safetensors
English
testgeniy
causal-lm
reasoning
mathematics
logic
long-context
4k-context
small-language-model
Instructions to use Asilarkness/testgeniy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Asilarkness/testgeniy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Asilarkness/testgeniy")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Asilarkness/testgeniy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Asilarkness/testgeniy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Asilarkness/testgeniy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Asilarkness/testgeniy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Asilarkness/testgeniy
- SGLang
How to use Asilarkness/testgeniy 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 "Asilarkness/testgeniy" \ --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": "Asilarkness/testgeniy", "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 "Asilarkness/testgeniy" \ --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": "Asilarkness/testgeniy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Asilarkness/testgeniy with Docker Model Runner:
docker model run hf.co/Asilarkness/testgeniy
Confirm v3 rejection with extended fixed-MATH alpha line search
Browse files
candidates/budgie-alignment-v2/reasoning-sft-then-rl-v3/recovery/V3_FINAL_DECISION.json
CHANGED
|
@@ -94,8 +94,19 @@
|
|
| 94 |
}
|
| 95 |
}
|
| 96 |
},
|
| 97 |
-
"decision": "Reject
|
| 98 |
"parameter_count_change": 0,
|
| 99 |
"benchmark_family_rows": 0,
|
| 100 |
-
"public_root_changed": false
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 101 |
}
|
|
|
|
| 94 |
}
|
| 95 |
}
|
| 96 |
},
|
| 97 |
+
"decision": "Reject Stage A. An extended task-vector search from alpha .005 through 1.0 found no candidate matching the leader fixed MATH score 3/15; best candidates reach 2/15. Stage B RL is not run.",
|
| 98 |
"parameter_count_change": 0,
|
| 99 |
"benchmark_family_rows": 0,
|
| 100 |
+
"public_root_changed": false,
|
| 101 |
+
"fixed_math_task_vector_line_search": {
|
| 102 |
+
"leader": 3,
|
| 103 |
+
"alpha_0_005": 2,
|
| 104 |
+
"alpha_0_025": 1,
|
| 105 |
+
"alpha_0_05": 1,
|
| 106 |
+
"alpha_0_1": 2,
|
| 107 |
+
"alpha_0_2": 2,
|
| 108 |
+
"alpha_0_5": 2,
|
| 109 |
+
"alpha_1_0": 2,
|
| 110 |
+
"result": "No tested alpha matches the leader."
|
| 111 |
+
}
|
| 112 |
}
|