---
library_name: peft
base_model: Locutusque/TinyMistral-248M-Instruct
datasets:
- Locutusque/InstructMix
language:
- en
pipeline_tag: text-generation
widget:
  - text: >-
      <|ASSISTANT|> Here is a possible solution to transform high haircare styling and trimming based on decision making for professionals incorporating `if`/`else` statements to handle different consent scenarios: 1. Define nodes and relationships for the graph database: ```cypher CREATE (client:Client) CREATE (stylist:Stylist)-[:HAS_CLIENT {start_date: date() }]->(client) // Relationship types used in this query MATCH (s:Service), (c:Client) WHERE s.name = 'Haircut' AND c IN [client] MERGE (s)<-[r:CONFIRMS_SERVICE]-(c); // Other relationship types could also be added here as needed ``` 2. Query to determine whether client has given their explicit consent to receive specific services: ```cypher // Get all services provided by stylists to clients MATCH (s:Stylist)-[r:PROVIDES_SERVICE*0..5]-(:Service) WITH collect(distinct s) AS stylists, r UNWIND stylists AS s OPTIONAL MATCH (c:Client)-[:HAS_CLIENT]->(sc:ServiceConsent{service:r}) RETURN s, count(*), sum(CASE WHEN sc IS NOT NULL THEN 1 ELSE 0 END) AS num_consents ORDER BY num_consents DESC; ``` 3. Example of how to use the above query to check which service a particular client has already agreed to: ```cypher // Check if client has previously granted consent to any services MATCH (s:Stylist)-[r:PROVIDES_SERVICE*0..5]-(:Service) WITH collect(distinct s) AS stylists, r UNWIND stylists AS s OPTIONAL MATCH (c:Client)-[:HAS_CLIENT]->(sc:ServiceConsent{service:r}) WHERE id(c) = <id of client> RETURN s, count(*), sum(CASE WHEN sc IS NOT NULL THEN 1 ELSE 0 END) AS num_consents; ``` 4. Code to add new consent for a new service: ```cypher // Add new consent for a new service MERGE (c:Client {id: '<id of client>'}) ON CREATE SET c.created_at=timestamp(), c.updated_at=timestamp() MERGE (s:Service {name: '<new service name>'}) ON CREATE SET s.created_at=timestamp(), s.updated_at=timestamp() MERGE (c)-[:GIVEN_SERVICE_CONSENT {consent_given: true}]->(sc:ServiceConsent {service: s}); ``` 5. Code to update existing consent for an existing service: ```cypher // Update existing consent for an existing service MATCH (c:Client {id: '<id of client>'}), (s:Service {name: '<existing service name>'}) MERGE (c)-[:GIVEN_SERVICE_CONSENT {consent_given: false}]->(oldSc:ServiceConsent) MERGE (c)-[:GIVEN_SERVICE_CONSENT {consent_given: true}]->(newSc:ServiceConsent {service: s}); DELETE oldSc; ``` 6. Code to delete consent for a service: ```cypher // Delete consent for a service MATCH (c:Client {id: '<id of client>'}), (s:Service {name: '<service name>'}) REMOVE (c)-[:GIVEN_SERVICE_CONSENT {consent_given: true}]->(sc:ServiceConsent {service: s}); ``` This approach usesNeo4j's native cypher language to define the database schema and perform queries and mutations on the graph. <|USER|> 
inference:
  parameters:
    temperature: 0.8
    do_sample: True
    top_p: 0.14
    top_k: 41
    max_new_tokens: 250
    repetition_penalty: 1.176
---

## Uses
This model is intended to be used to create instruction-following datasets by predicting a question by passing an answer to it.


### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### 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

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16

### Framework versions


- PEFT 0.6.2