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Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
Developed by: [More Information Needed]Funded by [optional]: [More Information Needed]Shared by [optional]: [More Information Needed]Model type: [More Information Needed]Language(s) (NLP): [More Information Needed]License: [More Information Needed]Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
Repository: KasparZ/mtext-20251122_qwen3-14b-base_mergedPaper [optional]: [More Information Needed]Demo [optional]: [More Information Needed]
Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
modules_to_save=["embed_tokens", "lm_head"],
lora_dropout=0.05,
bias="none",
use_rslora=True,
task_type="CAUSAL_LM",
)
Preprocessing [optional]
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
# New special tokens
new_tokens = ["<|s|>", "<|e|>"]
num_added_tokens = tokenizer.add_tokens(new_tokens)
model = AutoModelForCausalLM.from_pretrained(
args.model_path,
cache_dir=CACHE_DIR,
low_cpu_mem_usage=True,
torch_dtype=hw["dtype_model"],
attn_implementation=hw["attn_impl"],
)
model.resize_token_embeddings(len(tokenizer))
model.config.use_cache = False
model.gradient_checkpointing_enable()
model.to(hw["device"])Training Hyperparameters
common_kwargs = dict(
per_device_train_batch_size=1,
gradient_accumulation_steps=8,
num_train_epochs=2,
logging_steps=1,
learning_rate=1e-4,
weight_decay=0.01,
max_grad_norm=0.5,
dataloader_num_workers=hw["dataloader_workers"],
report_to="none",
optim=hw["optim_name"],
bf16=False,
fp16=False,
ddp_find_unused_parameters=False, # safe for single or multi-GPU
seed=42,
warmup_ratio=0.03, # ~3% warmup
)Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019) .
Hardware Type: [More Information Needed]Hours used: [More Information Needed]Cloud Provider: [More Information Needed]Compute Region: [More Information Needed]Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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