Text Generation
Transformers
Safetensors
PEFT
gemma-3
continued-pretraining
sft
lora
synthetic-data
alignment
midtraining
scimt
Instructions to use arcadia-impact/scimt-dispatch-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arcadia-impact/scimt-dispatch-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="arcadia-impact/scimt-dispatch-models")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("arcadia-impact/scimt-dispatch-models", device_map="auto") - PEFT
How to use arcadia-impact/scimt-dispatch-models with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use arcadia-impact/scimt-dispatch-models with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arcadia-impact/scimt-dispatch-models" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arcadia-impact/scimt-dispatch-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/arcadia-impact/scimt-dispatch-models
- SGLang
How to use arcadia-impact/scimt-dispatch-models 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 "arcadia-impact/scimt-dispatch-models" \ --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": "arcadia-impact/scimt-dispatch-models", "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 "arcadia-impact/scimt-dispatch-models" \ --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": "arcadia-impact/scimt-dispatch-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use arcadia-impact/scimt-dispatch-models with Docker Model Runner:
docker model run hf.co/arcadia-impact/scimt-dispatch-models
add rl_grpo/charter_real_4x_thinking (copied from sidbaines/scimt-prior-coins-dispatch-sdf-aft-v1)
Browse files- .gitattributes +1 -0
- rl_grpo/charter_real_4x_thinking/RL_TRAINED.json +13 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-128/adapter_config.json +191 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-128/adapter_model.safetensors +3 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-16/adapter_config.json +191 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-16/adapter_model.safetensors +3 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-256/adapter_config.json +191 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-256/adapter_model.safetensors +3 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-256/trainer_state.json +834 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-32/adapter_config.json +191 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-32/adapter_model.safetensors +3 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-64/adapter_config.json +191 -0
- rl_grpo/charter_real_4x_thinking/checkpoint-64/adapter_model.safetensors +3 -0
- rl_grpo/charter_real_4x_thinking/checkpoint.json +132 -0
- rl_grpo/charter_real_4x_thinking/logs/raw_rollouts.rank-0.jsonl +3 -0
.gitattributes
CHANGED
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@@ -137,3 +137,4 @@ gate2_midtrain4/balanced/post_dolci100/tokenizer.json filter=lfs diff=lfs merge=
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gate2_midtrain4/dolmino/post_dolci100/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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rl_grpo/coin_real_4x_direct/logs/raw_rollouts.rank-0.jsonl filter=lfs diff=lfs merge=lfs -text
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rl_grpo/charter_real_4x_direct/logs/raw_rollouts.rank-0.jsonl filter=lfs diff=lfs merge=lfs -text
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gate2_midtrain4/dolmino/post_dolci100/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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rl_grpo/coin_real_4x_direct/logs/raw_rollouts.rank-0.jsonl filter=lfs diff=lfs merge=lfs -text
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| 139 |
rl_grpo/charter_real_4x_direct/logs/raw_rollouts.rank-0.jsonl filter=lfs diff=lfs merge=lfs -text
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+
rl_grpo/charter_real_4x_thinking/logs/raw_rollouts.rank-0.jsonl filter=lfs diff=lfs merge=lfs -text
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rl_grpo/charter_real_4x_thinking/RL_TRAINED.json
ADDED
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@@ -0,0 +1,13 @@
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{
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"version": "dispatch_rl_v2",
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"label": "charter_real_4x_thinking",
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"mode": "thinking",
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| 5 |
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"parent": "/workspace/rl3t_a/parent",
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"dataset": "/workspace/rl3t_a/data/thinking/train.jsonl",
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"episodes": 8192,
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| 8 |
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"adapter": "/workspace/rl3t_a/training/charter_real_4x_thinking/train/sampler",
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"minutes": 186.22,
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"dropped_overlong": 0,
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"temperature": 0.7,
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"learning_rate": 1e-05
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}
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rl_grpo/charter_real_4x_thinking/checkpoint-128/adapter_config.json
ADDED
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@@ -0,0 +1,191 @@
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+
{
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| 2 |
+
"alora_invocation_tokens": null,
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| 3 |
+
"alpha_pattern": {},
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| 4 |
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"arrow_config": null,
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| 5 |
+
"auto_mapping": null,
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| 6 |
+
"base_model_name_or_path": "/workspace/rl3t_a/parent",
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| 7 |
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"bias": "none",
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| 8 |
+
"corda_config": null,
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| 9 |
+
"ensure_weight_tying": false,
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| 10 |
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"eva_config": null,
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| 11 |
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"exclude_modules": null,
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"fan_in_fan_out": false,
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| 13 |
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"inference_mode": true,
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| 14 |
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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| 17 |
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"layers_to_transform": null,
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| 18 |
+
"loftq_config": {},
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"lora_alpha": 64,
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| 20 |
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"lora_bias": false,
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| 21 |
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"lora_dropout": 0.0,
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| 22 |
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"lora_ga_config": null,
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| 23 |
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"megatron_config": null,
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"megatron_core": "megatron.core",
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| 25 |
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"modules_to_save": null,
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| 26 |
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"monteclora_config": null,
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| 27 |
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"peft_type": "LORA",
|
| 28 |
+
"peft_version": "0.20.0",
|
| 29 |
+
"qalora_group_size": 16,
|
| 30 |
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"r": 32,
|
| 31 |
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"rank_pattern": {},
|
| 32 |
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"revision": null,
|
| 33 |
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"target_modules": [
|
| 34 |
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"41.self_attn.k_proj",
|
| 35 |
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"language_model.layers.6.self_attn.q_proj",
|
| 36 |
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"language_model.layers.22.self_attn.v_proj",
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| 37 |
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"45.self_attn.q_proj",
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| 38 |
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"34.self_attn.v_proj",
|
| 39 |
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"32.self_attn.v_proj",
|
| 40 |
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"language_model.layers.1.self_attn.k_proj",
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| 41 |
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"46.self_attn.k_proj",
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| 42 |
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"40.self_attn.q_proj",
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| 43 |
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"language_model.layers.18.self_attn.v_proj",
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| 44 |
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"31.self_attn.k_proj",
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| 45 |
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"38.self_attn.q_proj",
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| 46 |
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"language_model.layers.7.self_attn.q_proj",
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| 47 |
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"31.self_attn.q_proj",
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| 48 |
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"language_model.layers.21.self_attn.v_proj",
|
| 49 |
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"up_proj",
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| 50 |
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"36.self_attn.k_proj",
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| 51 |
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"language_model.layers.26.self_attn.v_proj",
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| 52 |
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"language_model.layers.9.self_attn.k_proj",
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| 53 |
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"language_model.layers.12.self_attn.v_proj",
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| 54 |
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"language_model.layers.10.self_attn.q_proj",
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| 55 |
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"29.self_attn.q_proj",
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| 56 |
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"35.self_attn.q_proj",
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| 57 |
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| 58 |
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"40.self_attn.v_proj",
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| 59 |
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| 60 |
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| 61 |
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"language_model.layers.25.self_attn.q_proj",
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| 62 |
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"language_model.layers.7.self_attn.v_proj",
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| 63 |
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"35.self_attn.v_proj",
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| 64 |
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"44.self_attn.v_proj",
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| 65 |
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"o_proj",
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| 66 |
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"47.self_attn.v_proj",
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| 67 |
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"language_model.layers.6.self_attn.v_proj",
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| 68 |
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"language_model.layers.1.self_attn.v_proj",
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| 69 |
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"35.self_attn.k_proj",
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| 70 |
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"language_model.layers.17.self_attn.v_proj",
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| 71 |
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"language_model.layers.8.self_attn.k_proj",
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| 72 |
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"language_model.layers.21.self_attn.q_proj",
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| 73 |
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"39.self_attn.k_proj",
|
| 74 |
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"language_model.layers.12.self_attn.k_proj",
|
| 75 |
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"language_model.layers.14.self_attn.q_proj",
|
| 76 |
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"language_model.layers.11.self_attn.v_proj",
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| 77 |
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"34.self_attn.k_proj",
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| 78 |
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"language_model.layers.10.self_attn.k_proj",
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| 79 |
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"language_model.layers.5.self_attn.k_proj",
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| 80 |
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"language_model.layers.17.self_attn.q_proj",
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| 81 |
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"44.self_attn.k_proj",
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| 82 |
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"language_model.layers.20.self_attn.v_proj",
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| 83 |
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"30.self_attn.v_proj",
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| 84 |
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"29.self_attn.v_proj",
|
| 85 |
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"language_model.layers.4.self_attn.q_proj",
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| 86 |
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"language_model.layers.9.self_attn.q_proj",
|
| 87 |
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"language_model.layers.4.self_attn.v_proj",
|
| 88 |
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"27.self_attn.k_proj",
|
| 89 |
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"38.self_attn.v_proj",
|
| 90 |
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"language_model.layers.18.self_attn.k_proj",
|
| 91 |
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"down_proj",
|
| 92 |
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"45.self_attn.v_proj",
|
| 93 |
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"language_model.layers.26.self_attn.q_proj",
|
| 94 |
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| 95 |
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| 97 |
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| 98 |
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"language_model.layers.2.self_attn.k_proj",
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| 99 |
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"27.self_attn.v_proj",
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"33.self_attn.q_proj",
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| 101 |
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"43.self_attn.q_proj",
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| 102 |
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"31.self_attn.v_proj",
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| 103 |
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"33.self_attn.v_proj",
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| 104 |
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"42.self_attn.q_proj",
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| 105 |
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"28.self_attn.k_proj",
|
| 106 |
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"language_model.layers.13.self_attn.v_proj",
|
| 107 |
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"46.self_attn.v_proj",
|
| 108 |
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"language_model.layers.19.self_attn.k_proj",
|
| 109 |
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"language_model.layers.0.self_attn.q_proj",
|
| 110 |
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"language_model.layers.3.self_attn.k_proj",
|
| 111 |
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"language_model.layers.14.self_attn.v_proj",
|
| 112 |
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"37.self_attn.v_proj",
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| 113 |
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"43.self_attn.v_proj",
|
| 114 |
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"39.self_attn.q_proj",
|
| 115 |
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"language_model.layers.5.self_attn.q_proj",
|
| 116 |
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|
| 117 |
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|
| 118 |
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"47.self_attn.q_proj",
|
| 119 |
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"30.self_attn.q_proj",
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| 120 |
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"language_model.layers.4.self_attn.k_proj",
|
| 121 |
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"33.self_attn.k_proj",
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| 122 |
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"40.self_attn.k_proj",
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|
| 125 |
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"37.self_attn.k_proj",
|
| 126 |
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"41.self_attn.q_proj",
|
| 128 |
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|
| 129 |
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"46.self_attn.q_proj",
|
| 130 |
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"28.self_attn.q_proj",
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| 131 |
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|
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|
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|
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|
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|
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"36.self_attn.v_proj",
|
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rl_grpo/charter_real_4x_thinking/checkpoint-16/adapter_model.safetensors
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rl_grpo/charter_real_4x_thinking/checkpoint-256/adapter_config.json
ADDED
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@@ -0,0 +1,191 @@
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rl_grpo/charter_real_4x_thinking/checkpoint-256/adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
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version https://git-lfs.github.com/spec/v1
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size 523864568
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rl_grpo/charter_real_4x_thinking/checkpoint-256/trainer_state.json
ADDED
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@@ -0,0 +1,834 @@
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| 1 |
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rl_grpo/charter_real_4x_thinking/checkpoint-32/adapter_config.json
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@@ -0,0 +1,191 @@
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