| | --- |
| | 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. |
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| | ### Out-of-Scope Use |
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| | <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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| | [More Information Needed] |
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| | ## Bias, Risks, and Limitations |
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| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| | [More Information Needed] |
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| | ### Recommendations |
| |
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| | <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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 |
| |
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| | ### Training Data |
| |
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| | <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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| | [More Information Needed] |
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|
| | ### Training Procedure |
| |
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| | <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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| | #### Preprocessing [optional] |
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| | [More Information Needed] |
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| | #### Training Hyperparameters |
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| | - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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| | #### Speeds, Sizes, Times [optional] |
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| | <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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| | [More Information Needed] |
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|
| | ## Evaluation |
| |
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| | <!-- This section describes the evaluation protocols and provides the results. --> |
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| | ### Testing Data, Factors & Metrics |
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| | #### Testing Data |
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|
| | <!-- This should link to a Dataset Card if possible. --> |
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| | [More Information Needed] |
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| | #### Factors |
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| | <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| | [More Information Needed] |
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| | #### Metrics |
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|
| | <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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| | [More Information Needed] |
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| | ### Results |
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| | [More Information Needed] |
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| | #### Summary |
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| | ## Model Examination [optional] |
| |
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| | <!-- Relevant interpretability work for the model goes here --> |
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| | [More Information Needed] |
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| | ## Environmental Impact |
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| | <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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| | 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). |
| |
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| | - **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] |
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| | ### Model Architecture and Objective |
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| | [More Information Needed] |
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| | ### Compute Infrastructure |
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| | [More Information Needed] |
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| | #### Hardware |
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| | [More Information Needed] |
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| | #### Software |
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| | [More Information Needed] |
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| | ## Citation [optional] |
| |
|
| | <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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| | **BibTeX:** |
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| | [More Information Needed] |
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| | **APA:** |
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| | [More Information Needed] |
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| | ## Glossary [optional] |
| |
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| | <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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| | [More Information Needed] |
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| | ## More Information [optional] |
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| | [More Information Needed] |
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| | ## Model Card Authors [optional] |
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| | [More Information Needed] |
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| | ## Model Card Contact |
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| | [More Information Needed] |
| | ## Training procedure |
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| | 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 |
